Nuance Communications, Inc.

Cerence Operating Company

Scansoft, Inc.

Vincent Pollet

William F Ganong, Iii

Alexander Sorin

Andrew Paul Breen

Slava Shechtman

Geert Coorman

Bert Van Coile

Carl Benjamin Quillen

Mario De Bock

Jan De Moortel

Philip Charles Woodland

Stefaan Van Gerven

Enrico Zovato

Steven Hoskins

A method for removing private data from an acoustic model includes capturing speech from a large population of users, creating a text-to-speech voice from at least a portion of the large population of users, discarding speech data from a database of speech, creating text-to-speech waveforms from the text-to-speech voice and the new database of speech with the discarded speech data and generating an automatic speech recognition model using the text-to-speech waveforms.

System and method using parameterized speech synthesis to train acoustic models

Aspects of the disclosure are related to synthesizing speech or other audio based on input data. Additionally, aspects of the disclosure are related to using one or more recurrent neural networks. For example, a computing device may receive text input; may determine features based on the text input; may provide the features as input to an recurrent neural network; may determine embedded data from one or more activations of a hidden layer of the recurrent neural network; may determine speech data based on a speech unit search that attempts to select, from a database, speech units based on the embedded data; and may generate speech output based on the speech data.

Speech synthesis using one or more recurrent neural networks

Techniques for performing multi-style speech synthesis. The techniques include using at least one computer hardware processor to perform: obtaining input comprising text and an identification of a desired speaking style to use in rendering the text as speech; identifying a plurality of speech segments for use in synthesizing the text as speech, the identifying comprising identifying a first speech segment recorded and/or synthesized in a first speaking style that is different from the desired speaking style based at least in part on a measure of similarity between the desired speaking style and the first speaking style; synthesizing speech from the text in the desired speaking style at least in part by using the first speech segment; and outputting the synthesized speech.

Systems and methods for multi-style speech synthesis

Techniques for performing multi-style speech synthesis. The techniques include using at least one computer hardware processor to perform: obtaining input comprising text and an identification of a first speaking style to use in rendering the text as speech; identifying a plurality of speech segments for use in rendering the text as speech, the identified plurality of speech segments comprising a first speech segment having the first speaking style and a second speech segment having a second speaking style different from the first speaking style; and rendering the text as speech having the first speaking style, at least in part, by using the identified plurality of speech segments.

An embodiment according to the invention provides a capability of automatically predicting how favorable a given speech signal is for statistical modeling, which is advantageous in a variety of different contexts. In Multi-Form Segment (MFS) synthesis, for example, an embodiment according to the invention uses prediction capability to provide an automatic acoustic driven template versus model decision maker with an output quality that is high, stable and depends gradually on the system footprint. In speaker selection for a statistical Text-to-Speech synthesis (TTS) system build, as another example context, an embodiment according to the invention enables a fast selection of the most appropriate speaker among several available ones for the full voice dataset recording and preparation, based on a small amount of recorded speech material.

System and method for automatic prediction of speech suitability for statistical modeling

A speech synthesis system and method is described. A speech segment database references speech segments having various different speech representational structures. A speech segment selector selects from the speech segment database a sequence of speech segment candidates corresponding to a target text. A speech segment sequencer generates from the speech segment candidates sequenced speech segments corresponding to the target text. A speech segment synthesizer combines the selected sequenced speech segments to produce a synthesized speech signal output corresponding to the target text.

Synthesis by generation and concatenation of multi-form segments

A system and method generate synthesized speech through concatenation of speech segments that are derived from a large prosodically-rich corpus of speech segments including using an additional dictionary of speech segment identifier sequences.

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Astene, Belgium

Vincent Pollet